检索结果-内蒙古大学图书馆

1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yi, Da Guo, Chao Bai, Tianxiang Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods. © 2021 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

引用

19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

AUTOSIM:Automated Urban Traffic Operation Simulation via Meta-Learning

引用

IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1871-1881页

作者： Yuanqi Qin Wen Hua Junchen Jin Jun Ge Xingyuan Dai Lingxi Li Xiao Wang Fei-Yue Wang Zhejiang Lab Hangzhou 310030China ECARX Technology Co. Ltd.Shanghai 201811China IEEE Zhejiang Supcon Information Company Ltd. Hangzhou 310052China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Indiana University-Purdue University Indianapolis(IUPUI) IndianapolisIndiana 46202 USA School of Artificial Intelligence Anhui UniversityHefei 230039China

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic *** has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time *** cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry *** a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal ***,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a *** computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.

关键词： Conditional generative adversarial network signalized urban networks short-term link speed prediction

来源：评论

学校读者我要写书评

暂无评论

Short-Term Wind Power Prediction Based on KNN-BiGRU

Short-Term Wind Power Prediction Based on KNN-BiGRU

引用

Power Engineering (ICPE), International Conference on

作者： Xinyu Fan Zhenlong Wu Guibin Bian Yanhong Liu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331531140

ISBN: (纸本)9798331531157

Wind power generations have received widespread concern recently, however, due to the continuity of time series, ordinary machine learning models cannot learn the dependencies of continuous time series data well. To better predict the time series and improve the prediction effect of wind power, a shortterm wind power prediction method based on K-Nearest Neighbor(KNN) and Bidirectional Gated Recurrent Unit(BiGRU) is proposed. Firstly, KNN is used to detect outliers in the wind speed-power sequence in the field collected data, and then the wind speed value of all abnormal data is modified to the mean value of wind speed. Then the modified data is input to the bidirectional gated recurrent unit for prediction. The bidirectional gated recurrent unit can efficiently learn the context information of the data and improve the prediction effect. The experimental results show that the proposed prediction method based on KNN-BiGRU obtains a good prediction effect on a single unit. Compared with other methods, it has the best prediction effect on multi-unit where the accuracy rate is $\mathbf{8 4. 3 0 4} \%$.

关键词： Training Power engineering Accuracy Wind speed Time series analysis Nearest neighbor methods Wind power generation Predictive models Data models Optimization

来源：评论

学校读者我要写书评

暂无评论

NA-CPG: A robust and stable rhythm generator for robot motion control

引用

Biomimetic Intelligence & Robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

来源：评论

学校读者我要写书评

暂无评论

SIM: A Scenario IMagination Based Deep Reinforcement Learning Method for Outdoor Transportation Environment Exploration 11

SIM: A Scenario IMagination Based Deep Reinforcement Learnin...

引用

11th IEEE Data Driven Control and Learning systems Conference, DDCLS 2022

作者： Li, Haoran Zhang, Qichao Chen, Yaran Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Autonomous exploration is very important for robotics, especially for mapping, navigation, and planning in an unknown environment. In recent years, automatic exploration methods in the indoor environment have been extensively studied, but there is little research on exploration in the outdoor transportation environment. Due to the limitations of outdoor traffic rules and the scale of the environment, the methods for the indoor environment are difficult to apply to the transportation environment. Aiming at exploration in the transportation environment, this paper proposes a deep reinforcement learning algorithm based on Scenario IMagination(SIM), which has two important components: 1) a mid-level action space, which combines the classical robot control algorithm, addressing the inefficient learning and unstable navigation of deep reinforcement learning algorithms in automatic exploration. With this action space, the deep reinforcement learning algorithm achieves excellent exploration performance in both normal scale environments and large-scale branchless environments;2) a scenarios buffer, which relieves hard exploration problems of deep reinforcement learning due to serious imbalances of samples in large-scale multibranch scenarios. Compared to the map-less navigation approaches, SIM achieves excellent exploration performance in large-scale multi-branch environments. © 2022 IEEE.

关键词： Navigation

来源：评论

学校读者我要写书评

暂无评论

A Preliminary Study on the Functional Coupling between Nerve and Blood Microcirculation for Applications in Rehabilitation Robots

A Preliminary Study on the Functional Coupling between Nerve...

引用

2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Qingge Dong, Yuanzhe Zhang, Yuxiang Wang, Xin Jiang, Naifu Huang, Jianping Cui, Han Tian, Lan Zheng, Yue Li, Xiangxin Wang, Lin Li, Guanglin Liang, Wenyuan Peng, Liang Fang, Peng Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China National Research Center for Rehabilitation Technical Aids Beijing100176 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350325706

Rehabilitation robots play an important role in the motor function rehabilitation for stroke survivors with hemiplegia. However, the rehabilitation effect of current robots is still limited partly because a single training of motor function can be strongly affected by the decreased blood supply function of the bedridden patients. This work proposed an approach to study the coupling relationship between the motor and blood supply functions by combining the synchronously recorded EEG and cerebral blood oxygen information, where the cerebrations in different movement paradigms were analyzed from an aspect of "functional coupling". The results show that the information of oxyhemoglobin concentration change (ΔHbO) can effectively indicate the cortex activation, and a stronger blood supply is needed in cortexes to perform body movements. The correlations within motor cortexes are significantly stronger than the ones between motor and prefrontal cortexes, and a higher resistance level of extremity training will cause stronger correlations. Calculation of transfer entropy (TE) shows that there exists a bidirectional information transmission between the electrophysiological and blood supply signals, and more information is transmitted always in the direction from ΔHbO to EEG than in the opposite direction. The information transmission or the coupling relationship can be significantly enhanced by extremity movements, and large TE values are achieved in the Theta, Beta and Gamma frequency bands of EEG that correspond to motor functions. This work has demonstrated the functional coupling between nerve and blood microcirculation, which would provide a technical guidance to improve the rehabilitation effect for current robot systems and have great application potentials. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel robotic visual perception framework for underwater operation

引用

Frontiers of Information Technology & Electronic Engineering 2022年第11期23卷 1602-1619页

作者： Yue LU Xingyu CHEN Zhengxing WU Junzhi YU Li WEN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Ytech Kuaishou TechnologyBeijing 100085China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.

关键词： Underwater operation Robotic perception Visual restoration Video object detection

来源：评论

学校读者我要写书评

暂无评论

Visual Grasping with Spectral Clustering and Heuristic Searching for Robot in Cluttered Environments

Visual Grasping with Spectral Clustering and Heuristic Searc...

引用

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Geng, Wenjie Cao, Zhiqiang Tang, Yingbo Wang, Shuo Jing, Fengshui The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

Grasping the target object is an essential requirement for the robot to provide better services. It becomes complicated especially in cluttered environments, which still remains challenging. This paper proposes a grasping chain generation solution that enables the robot to grasp the target after other obstruction objects are moved in a good order. SSD is firstly adopted to acquire the information of detectable objects and then Euclidean clustering is employed to obtain the untrained objects. After that, the minimum bounding box of each object is obtained, which is then projected on the plane and represented by a smooth differentiable minimum ellipse. On this basis, an information density kernel function is designed to express the interaction between objects. By abstracting each ellipse as a node of the graph whose edge weight is calculated by this kernel function, the whole scene is described in a form of graph. To simplify the complexity of the scene graph, we use spectral clustering algorithm to classify the objects, and the task-oriented objects graph is constructed according to the objects closely related to the target one. As a result, the searching space is reduced. With space division of task-oriented objects graph, each candidate grasping chain is iteratively extended by using the heuristic searching and the best chain with the shortest length is determined. The proposed method can deal with secondary obstruction and its effectiveness is testified by experiments. © 2022 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

An Automatic Hand Rehabilitation Assessment System: Implementation and Experiments 12

An Automatic Hand Rehabilitation Assessment System: Implemen...

引用

12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Wang, Yifan Cheng, Long Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The lack of rehabilitation therapists and the increase of disabled individuals have reduced the opportunities for patients to receive well rehabilitation assessment. Towards solving this challenge, establishing a therapist-assistance assessment system to reduce the medical resource consumption is demanding. This paper proposes an automatic hand rehabilitation assessment system based on the Leap Motion vision sensor and a self-designed hand strength measurement device. The assessment algorithm requires the patients to complete every motion given in Fugl-Meyer assessment scale (FMA), and automatically scores the completion level of each motion via recognizing different hand gestures and hand grasping strength. As illustrated by the experiments on ten volunteers, the proposed assessment system can obtain the reliable scores which are consistent with the ones manually obtained via FMA. © 2022 IEEE.

关键词： Patient rehabilitation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：